1. Field of the Invention
The invention relates to an array system architecture of processors having multiple parallel structures which in particular is suitable for the digital simulation of fluid flows.
The invention has applications in the field of data processing, particularly for data relative to fluid flows (water, air, gas, molten metals, etc.). It can e.g. be used in geology for studying the behaviour of porous media, the strength of materials for the study of the resistance of parts to fluid flows, in aerodynamics for atmospheric reentry problems, in chemistry for the study of catalytic phenomena, or finally metallurgy for the study and design of liquefied metal filtering means, etc.
2. Discussion of the Background
In the data processing field, it is often necessary to process inhomogeneous data structures, i.e. structures having on an overall basis identical behaviours, but whose local behaviours may differ. This is in particular the case for data relative to fluid flows, which generally have identical macroscopic behaviours, but which are dependent on the microscopic behaviours of the constituents of the fluid.
For studying the macroscopic behaviour of fluids, it is known to carry out a modelling thereof. This is done by discretizing the physical space into a two or three-dimensional network with unity spacing, in which each constituent molecule of the fluid is modelled by a point moving on a grid of discrete positions, i.e. the network. Each site of the network, i.e. each point of the grid, corresponds to a local value of several fields representing physical quantities, such as the particle density, flow, etc.
The time is also discretized for such a modelling. For each time interval, each site evolves following a law simulating the physical behaviour of the flow in question using the values taken by the fields in the vicinity thereof.
This evolution of the sites can be carried out in different ways. Conventionally it takes place by means of laws expressed in the form of partial differential equations, such as NAVIER-STOCKES equations, by means of a method of finite elements or finite differences.
This evolution can also be carried out by so-called gas-on-network methods consisting of simulating the macroscopic behaviour of the fluid by means of a mesoscopic space provided with "automaton-type" evolution laws, determined in such a way as to correspond to partial differential equations, such as NAVIER-STOCKES equations. This gas-on-network method consequently makes it possible to model on a microscopic scale the behaviour of a fluid seeking the similarity between the macroscopic behaviour of the gas and that of a real fluid.
Compared with conventional methods, these gas-on-network methods have the advantage of permitting much more varied applications as a result of the considerable adaptability capacity of the rules used in these methods for modelling various physical systems.
In gas-on-network methods, the propagation on the links of the network takes place at the speed of one network spacing per time unit. This propagation is synchronized, so that for each time interval all the particles having a non-zero speed are simultaneously displaced. This propagation phase is followed by a collision phase on each site, i.e. on each point of the grid or network. For this method, the collision rules consist of associating with each possible configuration representing an entry state of the particles on the site, another configuration which will represent the outlet state produced by the collision of the particles on this site. This simulation of the behaviour of the fluid is governed by collision rules differing as a function of the chosen model.
In gas-on-network methods, it is e.g. possible to use Boolean models. In this case, the collision rules used operate solely on binary variables and are in the form of collision tables representing all the possible cases. Thus, these collision tables summarize the evolution function of the system, which takes account of quantities retained in the studied system, such as the movement quantity and mass.
Such a Boolean gas-on-network method is generally performed by architectures of parallel structure processor systems performing a simultaneous processing on a large number of sites. It is known to install accelerating cards on working stations such as the CAM machines described by T. TOFFOLI and M. MARGOLUS in "Cellular Automa Machines", MIT Press, Cambridge, Mass., 1987, or the R.A.P. machines described by A. CLOUQUEUR and D. D'HUMIERES "R.A.P.: a family of cellular automaton machines for fluid dynamics", in Proceedings of the 12th Gwatt workshop on complex systems, Gwatt, Switzerland, October 1988.
This Boolean model gas-on-network method is easy to perform as a result of the use of collision tables. It is therefore fast and can be adapted to problems of different types. Moreover, the operations are carried out without information loss, so that said method is digitally stable. However, this method suffers from a major disadvantage with respect to the presence of digital noise found on the results obtained at the end of processing and the digital noise can disturb the extraction of the results.
The gas-on-network method can also use BOLTZMANN models. In this case, the collision rule consists of a local resolution of an equation of state. Contrary to the collision rules with respect to Boolean models, this collision rule based on BOLTZMANN models uses variables having real values representing particle distributions. Thus, this makes it necessary to carry out floating calculations, whose performance is difficult and burdensome. This method also requires the resolution of partial differential equations, which is also burdensome to perform. Moreover, this method permits a good precision of the calculations, due to the quasi-continuous aspect of the particle distributions forming said model, which ensures a reduced digital noise, but said calculations are not very stable.